Distributed Connection Interface for Cloud Data Capture
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Solution Overview
Problem
Current systems for data capture from multiple network service sources lack a scalable and flexible connection interface that can autonomously configure APIs from disparate sources, transform data into usable formats, and handle complex business scenarios effectively.
Innovation Solution
A highly scalable distributed connection interface that uses a distributed computational graph to manage connector workflows, enabling passive stream monitoring and event-driven data retrieval, with robust scripting capabilities for routing and transformation, and self-load balancing for high availability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a robust connection interface is provided to access multiple cloud-based services, then adaptability and versatility are improved, but device complexity increases due to the need to manage diverse APIs and authentication mechanisms
Solution Approach 1:
The patent implements a universal connection interface that can access multiple cloud-based services (Salesforce, Google, Microsoft, SAP, Oracle) through a single standardized API framework. This multi-functional interface eliminates the need for separate integration code for each service, providing adaptability across diverse platforms while reducing overall system complexity through code reuse and standardized authentication mechanisms.
Solution Approach 2:
The patent introduces an intermediary layer (connection interface) that mediates between the enterprise operating system and various cloud services. This intermediary handles the complexity of diverse APIs and authentication mechanisms internally, presenting a simplified interface to users while managing the underlying complexity through standardized protocols and centralized credential management.
2Productivity
If passive stream monitoring and event-driven data retrieval are implemented, then productivity is improved through automated data capture, but device complexity increases due to the need for multiple data retrieval mechanisms
Solution Approach 1:
The patent merges passive stream monitoring and event-driven data retrieval into a unified connection interface that supports both mechanisms simultaneously. This combination allows the system to automatically capture data through multiple pathways without requiring separate implementation layers, improving productivity through comprehensive data capture while managing complexity through integrated architecture.
Solution Approach 2:
The connection interface implements dynamic data retrieval by automatically selecting between passive stream monitoring and event-driven approaches based on the specific service and data requirements. This dynamic adaptation allows the system to optimize data capture methods in real-time, improving productivity while managing complexity through automated method selection rather than manual configuration.
3Reliability
If self-load balancing is implemented for high availability, then reliability is improved, but device complexity increases due to the need for distributed architecture and coordination mechanisms
Solution Approach 1:
The patent implements self-load balancing by segmenting the connection interface into multiple independent instances that can operate in parallel. Each instance handles a portion of the data retrieval workload independently, providing high availability through distributed architecture. The segmentation allows automatic failover and load distribution without requiring complex centralized coordination, managing complexity through modular design.
Solution Approach 2:
The connection interface implements self-load balancing through automated mechanisms that dynamically distribute workloads without external intervention. The system automatically monitors system state and adjusts load distribution in real-time, providing high availability through self-healing capabilities while managing complexity through automated decision-making algorithms rather than manual load management.
4Ease of operation
If robust scripting capabilities are provided for routing and transformation, then ease of operation is improved through automated data processing, but device complexity increases due to the need for scripting engine and transformation rules
Solution Approach 1:
The patent implements routing and transformation rules as pre-configured scripts that are established before data processing begins. These preliminary configurations define data flow paths and transformation logic in advance, allowing users to operate the system with simple parameter settings rather than complex real-time decisions. The scripting engine executes these pre-defined rules automatically, improving ease of operation while managing complexity through upfront configuration rather than runtime complexity.
Data Source
AI summary
A system and method for a highly scalable distributed connection interface for data capture from multiple network service sources. The connection interface is designed to enable simple to initiate, performant and highly available input/output from a large plurality of external networked service's and application's application programming interfaces (API) to the modules of an integrated predictive business operating system. To handle the high volume of information exchange, the connection interface is distributed and designed to be scalable and self-load-balancing. The connection interface possesses robust expressive scripting capabilities that allow highly specific handling rules to be generated for the routing, transformation, and output of data within the business operating system.